* João Rodrigues worked on [http://www.biopython.org/wiki/GSOC2010_Joao the Structural Biology module Bio.PDB] adding several features used in everyday structural bioinformatics.

Please read the [http://www.open-bio.org/wiki/Google_Summer_of_Code GSoC page at the Open Bioinformatics Foundation] and the main [http://code.google.com/soc Google Summer of Code] page for more details about the program. If you are interested in contributing as a mentor or student next year, please introduce yourself on the [http://biopython.org/wiki/Mailing_lists mailing list].

Please read the [http://www.open-bio.org/wiki/Google_Summer_of_Code GSoC page at the Open Bioinformatics Foundation] and the main [http://code.google.com/soc Google Summer of Code] page for more details about the program. If you are interested in contributing as a mentor or student next year, please introduce yourself on the [http://biopython.org/wiki/Mailing_lists mailing list].

Contents

2011 Project ideas

Biopython and PyCogent interoperability

Rationale

PyCogent and Biopython are two widely used toolkits for performing computational biology and bioinformatics work in Python. The libraries have had traditionally different focuses: with Biopython focusing on sequence parsing and retrieval and PyCogent on evolutionary and phylogenetic processing. Both user communities would benefit from increased interoperability between the code bases, easing the developing of complex workflows.

Approach

The student would focus on soliciting use case scenarios from developers and the larger communities associated with both projects, and use these as the basis for adding glue code and documentation to both libraries. Some use cases of immediate interest as a starting point are:

Integrate Biopython's phyloXML support, developed during GSoC 2009, with PyCogent.

Develop a standardised controller architecture for interrogation of genome databases by extending PyCogent's Ensembl code, including export to Biopython objects.

Challenges

This project provides the student with a lot of freedom to create useful interoperability between two feature rich libraries. As opposed to projects which might require churning out more lines of code, the major challenge here will be defining useful APIs and interfaces for existing code. High level inventiveness and coding skill will be required for generating glue code; we feel library integration is an extremely beneficial skill. We also value clear use case based documentation to support the new interfaces.

Medium to Hard. At a minimum, the student will need to be highly competent in Python and become familiar with core objects in PyCogent and Biopython. Sub-projects will require additional expertise, for instance: familiarity with concepts in phylogenetics and genome biology; understanding SQL dialects.

Galaxy phylogenetics pipeline development

Rationale

Galaxy is a popular web based interface for integrating biological tools and analysis pipelines. It is widely used by bench biologists for their analysis work, and by computational biologists for building interfaces to developed tools. HyPhy provides a popular package for molecular evolution and sequence statistical analysis, and the datamonkey.org server provides web based workflows to perform a number of common tasks with HyPhy. This project bridges these two complementary projects by bringing HyPhy workflows into the Galaxy system, standardizing these analyses on a widely used platform.

Approach

The student would bring existing workflows from datamonkey.org to Galaxy. The general approach would be to pick a datamonkey.org workflow, wrap the relevant tools using Galaxy's XML tool definition language, and implement a shared pipeline with Galaxy's workflow system. Functional tests will be developed for tools and workflows, along with high level documentation for end users.

Challenges

This project requires the student to become comfortable working in the existing Galaxy framework. This is a useful practical skill as Galaxy is widely used in the biological community. Similarly, the student should become familiar with the statistical evolutionary methods in HyPhy to feel comfortable wrapping and testing them in Galaxy. Since the tools would be widely used from the main Galaxy website and installed instances, we place a strong emphasis on students who feel comfortable building tests and examples that would ensure the developed workflows function as expected.

Medium to Hard. As envisioned, the project would involve implementing full phylogenetic pipelines with the Galaxy toolkits. This would require becoming familiar with the Galaxy tool integration framework as well as being comfortable with HyPhy tools and current pipelines. This would involve comfort with XML for developing the tool interfaces, and Python for integrating scripts and tests with Galaxy and HyPhy.

Accessing R phylogenetic tools from Python

Rationale

The R statistical language is a powerful open-source environment for statistical computation and visualization. Python serves as an excellent complement to R since it has a wide variety of available libraries to make data processing, analysis, and web presentation easier. The two can be smoothly interfaced using Rpy2, allowing programmers to leverage the best features of each language. Here we propose to build Rpy2 library components to help ease access to phylogenetic and biogeographical libraries in R.

Approach

Rpy2 contains higher level interfaces to popular R libraries. For instance, the ggplot2 interface allows python users to access powerful plotting functionality in R with an intuitive API. Providing similar high level APIs for biological toolkits available in R would help expose these toolkits to a wider audience of Python programmers. A nice introduction to phylogenetic analysis in R is available from Rich Glor at the Bodega Bay Marine Lab wiki. Some examples of R libraries for which integration would be welcomed are:

The student would have the opportunity to learn an available R toolkit, and then code in Python and R to make this available via an intuitive API. This will involve digging into the R code examples to discover the most useful parts for analysis, and then projecting this into a library that is intuitive to Python coders. Beyond the coding and design aspects, the student should feel comfortable writing up use case documentation to support the API and encourage its adoption.

Moderate. The project requires familiarity with coding in Python and R, and knowledge of phylogeny or biogeography. The student has plenty of flexibility to define the project based on their biological interests (e.g. microarrays and heatmaps); there is also the possibility to venture far into data visualization once access to analysis methods is made. GenGIS and can give ideas about what is possible.